What is the primary purpose of model tuning?

The primary purpose of model tuning is to enhance the model's predictive performance. This involves adjusting various parameters and configurations of the model to optimize its ability to accurately predict outcomes on unseen data. By meticulously modifying aspects such as learning rates, regularization methods, the number of trees in ensemble methods, or the depth of a decision tree, one can identify the optimal settings that yield the best performance metrics, such as accuracy, F1 score, or AUC-ROC.

In the context of machine learning, the goal is to find the best balance between bias and variance, ensuring that the model generalizes well to new data rather than just memorizing the training set. This iterative process is crucial for developing robust models that perform effectively in real-world applications.

Adding more features to the model and collecting more training data are strategies that can also improve performance, but they are not the primary focus of tuning. Visualizing the model's structure might help in understanding how it works but does not directly relate to tuning performance. Therefore, while those aspects can play a role in the overall modeling process, they are distinct from the essential goal of model tuning, which is centered on enhancing predictive capabilities.